Spatial Event Prediction by Combining Value Function Approximation and Case-Based Reasoning

Abstract

Abstract. This paper presents a new approach for spatial event prediction that combines a value function approximation algorithm and case-based reasoning predictors. Each of these predictors makes unique contributions to the overall spatial event prediction. The function value approximation prediction is particularly suitable to reasoning with geographical features such as the (x,y) coordinates of an event. The case-based prediction is particularly well suited to deal with non-geographical features such as the time of the event or income level of the population. We claim that the combination of these two predictors results in a significant improvement of the accuracy in the spatial event prediction compared to pure geographically-based predictions. We support our claim by reporting on an ablation study for the prediction of improvised explosive device (IED) attacks.

Adapting Spatial and Temporal Cases

“…Some recent work deals with a combination of CBR and spatial reasoning, for instance in order to improve web services for spatial information [18], or for spatial event prediction in hostile territories [19]. Older work already underlined the interest of CBR to analyse geographical data, e.g.…”